TY  - BOOK
AU  - Dahan,Haim
AU  - Cohen,Shahar
AU  - Rokach,Lior
AU  - Maimon,Oded
ED  - SpringerLink (Online service)
TI  - Proactive Data Mining with Decision Trees
T2  - SpringerBriefs in Electrical and Computer Engineering,
SN  - 9781493905393
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2014///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Computer science
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Information Storage and Retrieval
KW  - Information Systems Applications (incl. Internet)
N1  - Introduction -- Proactive Data Mining: A General Approach -- Proactive Data Mining Using Decision Trees -- Proactive Data Mining in the Real World: Case Studies -- Sensitivity Analysis of Proactive Data Mining -- Conclusions
N2  - This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students
UR  - http://dx.doi.org/10.1007/978-1-4939-0539-3
ER  -